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Artificial Intelligence Applications and Innovations AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB Halkidiki, Greece, September 27-30, 2012, Part II |
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Conference papers
Application of Conformal Prediction in QSAR
Abstract : QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using statistical learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity. However, predictions from a QSAR model are difficult to assess if their prediction intervals are unknown. In this paper we introduce conformal prediction into the QSAR field to address this issue. We apply support vector machine regression in combination with two nonconformity measures to five datasets of different sizes to demonstrate the usefulness of conformal prediction in QSAR modeling. One of the nonconformity measures provides prediction intervals with almost the same width as the size of the QSAR models’ prediction errors, showing that the prediction intervals obtained by conformal prediction are efficient and useful.
Cited literature [17 references]
https://hal.archives-ouvertes.fr/hal-01523068
Contributor : Hal Ifip <>
Submitted on : Tuesday, May 16, 2017 - 9:17:04 AM
Last modification on : Thursday, March 5, 2020 - 5:41:44 PM
Long-term archiving on: : Friday, August 18, 2017 - 12:30:25 AM
Contributor : Hal Ifip <>
Submitted on : Tuesday, May 16, 2017 - 9:17:04 AM
Last modification on : Thursday, March 5, 2020 - 5:41:44 PM
Long-term archiving on: : Friday, August 18, 2017 - 12:30:25 AM
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